Method for customizing motion characteristics of an autonomous vehicle for a user

ABSTRACT

One variation of a method for customizing motion characteristics of an autonomous vehicle for a user includes: accessing a baseline emotional state of the user following entry of the user into the autonomous vehicle at a first time proximal a start of a trip; during a first segment of the trip, autonomously navigating toward a destination location according to a first motion planning parameter, accessing a second emotional state of the user at a second time, detecting degradation of sentiment of the user based on differences between the baseline and second emotional states; and correlating degradation of sentiment of the user with a navigational characteristic of the autonomous vehicle; modifying the first motion planning parameter of the autonomous vehicle to deviate from the navigational characteristic; and, during a second segment of the trip, autonomously navigating toward the destination location according to the revised motion planning parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application claims the benefit of U.S. Provisional Application No.62/624,216, filed on 31 Jan. 2018, which is incorporated in its entiretyby this reference.

TECHNICAL FIELD

This invention relates generally to the field of autonomous vehicles andmore specifically to a new and useful method for customizing motioncharacteristics of an autonomous vehicle for a user in the field ofautonomous vehicles.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of a method;

FIG. 2 is a flowchart representation of one variation of the method; and

FIG. 3 is a flowchart representation of one variation of the method.

DESCRIPTION OF THE EMBODIMENTS

The following description of embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.Variations, configurations, implementations, example implementations,and examples described herein are optional and are not exclusive to thevariations, configurations, implementations, example implementations,and examples they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, example implementations, and examples.

1. Method

As shown in FIG. 1, a method S100 for customizing motion characteristicsof an autonomous vehicle for a user includes: detecting a first set ofbiosignals of the user following entry of the user into the autonomousvehicle at a first time proximal a start of a trip in Block S110; andidentifying a baseline emotional state of the user based on the firstset of biosignals in Block S112. The method S100 also includes, during afirst segment of the trip: autonomously navigating toward a destinationlocation of the trip according to a first motion planning parameter inBlock S120; detecting a second set of biosignals of the user at a secondtime in Block S130; identifying a second emotional state of the userbased on the second set of biosignals in Block S132; detecting adegradation of sentiment of the user based on a difference between thebaseline emotional state and the second emotional state in Block S140;and correlating the degradation of sentiment of the user with anavigational characteristic of the autonomous vehicle proximal thesecond time in Block S142. The method S100 further includes: modifyingthe first motion planning parameter of the autonomous vehicle to definea second motion planning parameter deviating from the navigationalcharacteristic in Block S150; and during a second segment of the trip,autonomously navigating toward the destination location according to thesecond motion planning parameter in Block S122.

One variation of the method S100 includes: accessing a baselineemotional state of the user following entry of the user into theautonomous vehicle at a first time proximal a start of a trip in BlockS112; and, during a first segment of the trip, generating a firstsequence of navigational actions according to a first motion planningparameter, executing the first sequence of navigational actions toautonomously navigate toward a destination location in Block S120,accessing a second emotional state of the user at a second time in BlockS132, detecting a degradation of sentiment of the user based on adifference between the baseline emotional state and the second emotionalstate in Block S140, and correlating the degradation of sentiment of theuser with a navigational characteristic of the autonomous vehicleproximal the second time in Block S142. This variation of the methodS100 also includes: modifying the first motion planning parameter of theautonomous vehicle to define a revised motion planning parameterdeviating from the navigational characteristic in Block S150; and,during a second segment of the trip, generating a second sequence ofnavigational actions according to the second motion planning parameterand executing the second sequence of number value actions toautonomously navigate toward the destination location in Block S122.

2. Applications

Generally, the method S100 can be executed by an autonomous vehicle toautomatically: collect biosignal data from a rider (hereinafter a“user”) occupying the autonomous vehicle over the course of a trip; totransform these biosignal data into sentiment (e.g., emotional state,feeling) of the user; to detect a negative change in the sentiment ofthe user (e.g., increased fear, stress, anxiety, nausea, or frustration,etc.); to correlate this negative change with certain navigationcharacteristics of the autonomous vehicle (e.g., speed, acceleration, ortrajectory); to modify a motion planning or navigational parameterimplemented by the autonomous vehicle in order to reverse this negativechange in the user's sentiment; and to implement this motion planning ornavigational parameter during a remainder of the trip (and during futuretrips involving this user) or until modified according to furthernegative changes in the user's sentiment. In particular, the autonomousvehicle can automatically execute Blocks of the method S100 in order toautomatically monitor a user inside the autonomous vehicle and rapidlyrespond to detected changes in the user's sentiment by automaticallyadjusting motion planning and/or navigational characteristics of theautonomous vehicle in order to maintain a high degree of user comfortthroughout the trip.

For example, the autonomous vehicle can: include an optical sensor;record images of the user during a trip through the optical sensor; andextract sentiments of the user from these images, as shown in FIG. 2.The autonomous vehicle can additionally or alternatively interface witha wearable device worn by the user to access biosignal data of the userand to transform these biosignal data into sentiments of the user. Inthis example, the user may exhibit increased levels of fear, stress, oranxiety as a result of perceived excess speed, perceived narrowavoidance of obstacles, or perceived aggressive navigation by theautonomous vehicle during a trip. If the autonomous vehicle detectsincreased levels of fear, stress, or anxiety—such as greater than apreset threshold change since the user entered the autonomousvehicle—over a short time interval (e.g., less than ten seconds), theautonomous vehicle can: associate this sentiment change with a singularaction by the autonomous vehicle; scan stored navigational events at theautonomous vehicle's path over this time interval for a navigationalevent likely to have triggered this sentiment change (e.g., proximity ofthe autonomous vehicle to a pedestrian, other vehicle, constructionzone, or other obstacle or contact with a curb or pothole); correlatethe change in sentiment with this navigational event; and modify motionplanning or navigation parameters of the autonomous vehicle in order toreduce frequency or intensity of similar events along the remainder ofthe trip (e.g., by increasing a minimum obstacle avoidance distance or“offset distance” for avoiding pedestrians and other vehicles,increasing a curb buffer distance, or decreasing the accelerationprofile of the vehicle).

In this example, if the autonomous vehicle detects an increased level ofnausea—such as greater than a preset threshold since the user enteredthe autonomous vehicle—in the user during the trip, the autonomousvehicle can automatically: set reduced peak permitted forward andlateral accelerations (and jerks) of the autonomous vehicle during theremainder of the trip; increase weight of a smoothing function for amotion planning model implemented by the autonomous vehicle in order tosmooth a path traversed by the autonomous vehicle during the remainderof the trip; and navigate to the user's specified duration according tothese revised navigation and motion planning parameters in order toreduce or abate further increase in nausea experienced by the user.Similarly, if the autonomous vehicle detects such an increased level ofuser nausea during the trip, the autonomous vehicle can automaticallyincrease penalties for: control inputs (e.g., steering wheel anglechange, accelerator position change, and brake position change); jerk(i.e., the derivative of acceleration); and/or rates of change of thesecontrol inputs. The autonomous vehicle can then apply these penalties toa motion planning algorithm implemented by the autonomous vehicle untilthese penalties are modified again responsive to data collected from theuser later in the trip.

In this example, the user may alternatively exhibit frustration due todriving characteristics of the autonomous vehicle, such as perceivedslow speeds, perceived slow accelerations, or perceived timidity of theautonomous vehicle. If the autonomous vehicle detects such increasedlevel of frustration—such as greater than a preset threshold changesince the user entered the autonomous vehicle—the autonomous vehicle canassociate this increased frustration with a perception of timidity andadjust motion planning and navigation parameters of the autonomousvehicle accordingly, such as by: setting increased peak permittedforward and lateral accelerations (and jerks) of the autonomous vehicleduring the remainder of the trip; decreasing weight of a smoothingfunction for a motion planning model implemented by the autonomousvehicle; and/or decreasing obstacle avoidance distances for curbs, othervehicles, and other obstacles. The autonomous vehicle can then navigateto the user's specified duration according to these revised navigationand motion planning parameters in order to reduce the user'sfrustration.

The autonomous vehicle can therefore actively monitor sentiment of auser occupying the autonomous vehicle while navigating to a destinationand adjust motion planning and/or navigation parameters implemented bythe autonomous vehicle in real-time responsive to changes in the user'ssentiment in order to maintain or improve the user's comfort throughoutthe trip.

The autonomous vehicle can also upload motion planning and/or navigationparameters customized for the user to a user profile associated with theuser, as shown in FIGS. 1 and 2. The user profile can then be accessedand implemented by the same or other autonomous vehicles during latertrips involving the user, thereby automatically customizing motionplanning and navigational characteristics of the autonomous vehicles forthe user immediately upon entry of the user into the autonomousvehicles. These autonomous vehicles can also execute Blocks of themethod S100 during these subsequent trips to further revise or updatemotion planning and/or navigation parameters for the user.

The autonomous vehicle can also detect multiple passengers occupying thepassenger compartment during a ride and implement similar methods andtechniques to generate customized planning and/or navigation parametersbased on biosignals read from these multiple passengers. In one example,the autonomous vehicle calculates a discrete set of revised motionplanning and/or navigation parameters for each occupant and thenimplements the revised planning and/or navigation parameters for theoccupant exhibiting greatest sensitivity (e.g., greatest nausea, thengreatest fear, then greatest frustration). Alternatively, the autonomousvehicle can combine (e.g., average) these discrete sets of revisedplanning and/or navigation parameters and implement these compositeplanning and/or navigation parameters, as described below. Yetalternatively, the autonomous vehicle can verify that a particularnavigational characteristic (e.g., proximity of the autonomous vehicleto a construction zone) was a primary cause of rider sentimentdegradation (e.g., increased rider anxiety) if the autonomous vehicledetects similar, concurrent rider sentiment degradation among most orall riders in the autonomous vehicle; the autonomous vehicle can thenmodify a corresponding motion planning parameter accordingly andimplement this modified motion planning parameter during the remainderof the trip or until modified again responsive to other emotion orsentiment changes in these riders.

The autonomous vehicle can additionally or alternatively: collectfeedback from the user during the trip, such as through the user'smobile computing device (e.g., smartphone) or through a display insidein the autonomous vehicle; correlate negative (and positive) feedbackprovided by the user to navigational events occurring at the autonomousvehicle; and automatically modify motion planning and/or navigationparameters of the autonomous vehicle accordingly substantially inreal-time. The autonomous vehicle (or a remote computer system) cansimilarly: collect feedback from the user following completion of thetrip; correlate negative (and positive) feedback provided by the user tonavigational events occurring at the autonomous vehicle during the trip;and automatically update the user's profile to reflect motion planningand/or navigation parameters that may reduce negative sentiments (andreinforce position sentiments) accordingly.

The autonomous vehicle can implement similar methods and techniques: todetect positive indicators of user comfort, such as slow and consistentheart rate, relaxed facial features, facial features correlated with joyor trust, user attention to other occupants in the autonomous vehicle(i.e., rather the to motion of the autonomous vehicle), and/ortransitions from negative comfort indicators to such positive comfortindicators; to reinforce current weights (or penalties) for controlinputs, jerk (i.e., the derivative of acceleration), and/or rates ofchange of control inputs; and to then apply these reinforced weights toa motion planning algorithm to maintain the user's current high level ofcomfort.

Therefore autonomous vehicles and/or a remote computer system canimplement Blocks of the method S100: to collect sentiment-related datafrom the user; to modify motion planning and/or navigation parametersaccording to these user sentiment data; and to implement these revisedmotion planning and/or navigation parameters in order to improve usercomfort for occupants during trips in these autonomous vehicles.

3. Autonomous Vehicle and Trip Initialization

The autonomous vehicle can include an autonomous passenger road vehicleconfigured to carry one or more passengers between pickup anddestination locations. For example, a user may enter a request—through anative rideshare application executing on her smartphone—for pickup at apickup location and navigation to a destination location. Upon receiptof this request, a remote computer system can assign this trip to theautonomous vehicle, dispatch the autonomous vehicle to the pickuplocation, and load the user's profile (or a default profile for theuser's first trip) onto the autonomous vehicle. Upon arrival at thepickup location, the autonomous vehicle can: confirm entry of the userinto the autonomous vehicle; record a baseline sentiment of the user, asdescribed below; load or calculate a route to the destination location;and resume navigation along the route according to the motion planningand/or navigation parameters stored in the user's profile. Theautonomous vehicle can then execute subsequent Blocks of the method S100to modify these motion planning and/or navigation parameters accordingto a change in the user's sentiment away from the baseline sentiment.

4. Real-Time Biosignal Collection and Emotion Detection

In one variation, the autonomous vehicle collects biosignal data fromthe user—through various sensors integrated into or connected to theautonomous vehicle—in real-time during the trip.

4.1 Wearable Device

In one implementation shown in FIG. 2, the system includes a wirelessradio and interfaces with a wearable device—including biometricsensors—worn by the user to collect user biometric data via the wirelessradio. For example, when creating an account for requesting rides withautonomous vehicles through the native application described above, thenative application can: query the user to confirm whether she wears awearable device; prompt the user to authorize the native application andthe remote computer system to access biometric data from this wearabledevice; and/or prompt the user to authorize autonomous vehicles toaccess these data during the user's trips. Following authorization forthe autonomous vehicle to access data from the user's wearable device,the remote computer system can store this authorization and anidentifier of the user's wearable device in the user's profile. Theautonomous vehicle can then pull or otherwise query the wearable devicefor biometric data from the user during the user's trip.

For example, at the beginning of the trip, the autonomous vehicle canautomatically connect to the user's wearable device (e.g., viashort-range wireless communication protocols); and the wearable devicecan regularly upload skin temperature, galvanic skin response, and/orheart rate, etc. to the autonomous vehicle (e.g., substantially inreal-time) once the autonomous vehicle confirms that the user hasentered the autonomous vehicle and until the autonomous vehicle arrivesat the user-specified destination. Alternatively, the autonomous vehiclecan automatically connect to the user's smartphone during the trip; theuser's wearable device can regularly upload data to her smartphone; andthe autonomous vehicle can access biosignal data from the user'swearable device via the smartphone.

Similarly, in response to entry of the user at the start of the trip,the autonomous vehicle can connect to a wearable device—including abiometric sensor and worn by the user—via short-range wirelesscommunication protocol. The autonomous vehicle can then access (or“download”) a first set of biosignals recorded and wirelessly broadcastby the wearable device proximal the start of the trip in Block S110;interpret a first user emotion from these first biosignals; later accessa second set of biosignals recorded and wirelessly broadcast by thewearable device at a second time during the trip in Block S130; and theninterpret a second user emotion from these second biosignals in BlockS132, as described below.

Therefore, throughout the trip, the autonomous vehicle can regularlypass these biosignal data—received from the user's wearable device—intoan emotion characterization model (e.g., a regression model or a neuralnetwork, etc.) that transforms these biosignal data into a sentiment(e.g., emotional state, feeling, and/or degree thereof) of the user. Theautonomous vehicle can then store these derived user sentimentsthroughout the trip, such as by annotating the autonomous vehicle'sgeoreferenced route throughout this trip with these sentiment values.Alternatively, the user's wearable device or smartphone can locallyinterpret user sentiment from these biosignal data and regularly servethese user sentiment values to the autonomous vehicle, such as once perfive-second interval. However, the autonomous vehicle can interface withthe user's wearable device in any other way to access or derive usersentiment values during the trip.

In this implementation, the autonomous vehicle can also store asentiment of the user at the beginning of the trip—such as recorded justafter the user entered the autonomous vehicle—as a baseline sentiment ofthe user. By then comparing the later wearable device-derived sentimentsof the user to this baseline sentiment, the autonomous vehicle can:determine whether the autonomous vehicle's navigation has lead to theuser's feelings of nausea, frustration, anxiety, or other discomfort(e.g., if the user's sentiment diminishes from the baselines sentimentover the course of the trip) or if these feelings of discomfort werepresent prior to the user's entry into the autonomous vehicle; andmodify its motion planning and/or navigation parameters accordingly.

4.2 Integrated Camera

In another implementation shown in FIG. 2, the autonomous vehicleincludes an interior-facing camera configured to record images (e.g.,frames in a color video stream) of the interior of the autonomousvehicle—occupied by the user—during the trip; and the autonomous vehicleextracts user sentiment from these images. In this implementation, theautonomous vehicle can include a color (e.g., RGB) camera with awide-angle lens and/or a depth sensor (e.g., a LIDAR or structured lightsensor) defining fields of view directed toward the autonomous vehicle'sinterior, such as toward passenger seat areas. For example, theautonomous vehicle can include one or more such optical sensors: coupledto an interior ceiling and directed downward; or coupled to thedashboard of the autonomous vehicle and directed rearward toward theautonomous vehicle's passenger compartment. In another example, theautonomous vehicle can include an optical sensor integrated into ahousing of an overhead interior map light within the autonomousvehicle's interior. However, the autonomous vehicle can include one ormore optical sensors of any other type and arranged in any other wayinside the autonomous vehicle.

In one implementation in which the autonomous vehicle includes multipleoptical sensors, these optical sensors can be arranged inside theautonomous vehicle such that their fields of view cooperatively cover alarge proportion of the passenger seat areas within the passengercompartment. In a scan cycle during the trip, the autonomous vehicle cantrigger each optical sensor to record a discrete image and then stitchthese discrete images into a composite 2D or 3D image based of theinterior of the autonomous vehicle on known relative positions of theseoptical sensors. The autonomous vehicle (or the remote computer system)can then process this composite image, as described below, to detect theuser and to qualify or quantify the user's sentiment during this scancycle. Once an image of the interior of the autonomous vehicle is thusrecorded, the autonomous vehicle can implement face detection, eyedetection, or other computer system techniques to detect the user insidethe passenger compartment. (The autonomous vehicle can also implementobject tracking to track the user over multiple contiguous images outputby the optical sensor(s)).

4.2.1 Photoplethysmography and Heart Rate

In one example implementation, the autonomous vehicle implementsphotoplethysmography or Eulerian video magnification to extract theuser's heart rate from a sequence of images of the interior of theautonomous vehicle. For example, once the autonomous vehicle detects theuser's face in a video feed output by the optical sensor, the autonomousvehicle can: detect cyclical variations in color intensity of pixelscorresponding to the user's face (e.g., “pulsatile photoplethysmographicsignals”) in the video feed; and transform a frequency of these cyclicalvariations into the user's heart rate. The autonomous vehicle can repeatthis process for a series of images recorded by the optical sensor overtime in order to develop a time series of the user's heart ratethroughout the trip.

Similarly, the autonomous vehicle can: record a first sequence of videoframes via a camera arranged in the autonomous vehicle and facing apassenger compartment in the autonomous vehicle when the user enters theautonomous vehicle at the start of the trip; detect a face of the userin the first sequence of video frames; detect a first sequence ofcyclical variations in color intensity of pixels depicting the face ofthe user in the first sequence of video frames; transform the firstsequence of cyclical variations in color intensity into a first heartrate of the user proximal the first time in Block S110; and identify abaseline emotion state—including a baseline anxiety level of the user—atthe first time based on the first heart rate in Block S112. At asubsequent time during the trip, the autonomous vehicle can then: recorda second sequence of video frames via the camera; detect the face of theuser in the second sequence of video frames; detect a second sequence ofcyclical variations in color intensity of pixels depicting the face ofthe user in the first sequence of video frames; transform the secondsequence of cyclical variations in color intensity into a second heartrate of the user proximal the second time in Block S130; and identify asecond emotion state—representing a second anxiety level of the user—atthe second time based on the second heart rate. The autonomous vehiclecan then: detect increased anxiety of the user based on the differencebetween the baseline emotional state and the second emotional state inBlock S140; and modify a motion planning parameter implemented by theautonomous vehicle in order to reduce frequency or magnitude ofnavigational characteristics occurring concurrently with this usersentiment degradation.

The autonomous vehicle can also correlate increases in heart rate of theuser with increased stress or fear. For example, the autonomous vehiclecan associate an increase in heart rate of over ten beats per minuteover a period of five seconds as fear or other negative emotionalresponse to a discrete event occurring—approximately concurrently—nearthe autonomous vehicle. In this example, the autonomous vehicle canassociate a similar increase in the user heart rate over a longer periodof time (e.g., one minute) with frustration. However, the autonomousvehicle can interpret absolute changes and/or rates of change in heartrate of the user with any other user sentiment in any other way. Theautonomous vehicle can then store these heart rate and/or user sentimentvalues, such as by tagging segments of the current route with theseheart rate and/or user sentiment values based on times that these heartrate values were recorded and geospatial locations of the autonomousvehicle at these times.

4.2.2 Eye Tracking and Nausea

In another example implementation, the autonomous vehicle can detect theuser's eyes in images output by the optical sensor, detect rapid eyemovements in these images, and interpret these rapid eye movements asdegrees of user nausea throughout the trip. For example, the autonomousvehicle can: implement eye tracking techniques to detect the user's eyesin a video feed output by the optical sensor; extract a rate (andamplitude) of oscillations of the users eyes from these images;correlate this rate (and amplitude) of eye oscillations with a degree towhich the user may be nauseated; and repeat this process throughout thetrip in order to develop a georeferenced time series of the degree ofthe user's nausea.

Similarly, the autonomous vehicle can: record a first sequence of videoframes via a camera arranged in the autonomous vehicle and facing apassenger compartment in the autonomous vehicle when the user enters theautonomous vehicle at the start of the trip; detect eyes of the user inthe first sequence of video frames; detect a first sequence of rapid eyemovements of the user in the first sequence of video frames in BlockS110; estimate a first degree of nausea of the user at the start of thetrip based on a first rate of eye movements in the first sequence ofrapid eye movements; and store the first degree of nausea of the user asthe baseline emotion state of the user in Block S112. Subsequently, theautonomous vehicle can: record a second sequence of video frames via thecamera; detect eyes of the user in the second sequence of video frames;detect a second sequence of rapid eye movements of the user in thesecond sequence of video frames in Block S130; estimate a second degreeof nausea of the user at this later time based on a second rate of eyemovements in the second sequence of rapid eye movements; and store thesecond degree of nausea of the user as the second emotion state of theuser in Block S132. Thus, the autonomous vehicle can detect increasednausea of the user based on a difference between the baseline emotionalstate and the second emotional state in Block S140.

4.2.3 Template Matching

In yet another example implementation shown in FIG. 1, the autonomousvehicle extracts user sentiment directly from images output by theoptical sensor. For example, the autonomous vehicle can: implementcomputer vision techniques—such as face detection and templatematching—to detect the user's face in an image recorded by the opticalsensor; and implement template matching techniques to match features ofthe user's face to a template image representing a known sentiment(e.g., calm, anxious, nauseated, fearful, etc.). Alternatively, theautonomous vehicle can: extract features from a region of the imagecoinciding with the user's face; and pass these features through anemotion characterization model to predict the user's sentiment at thetime this image was recorded.

The autonomous vehicle can repeat this process for a series of imagesrecorded by the optical sensor throughout the trip in order to develop ageoreferenced time series of user sentiments (and degrees of thesesentiments).

4.2.4 Sentiment Model

Similarly, the autonomous vehicle can: record a first sequence of videoframes via a camera arranged in the autonomous vehicle and facing apassenger compartment in the autonomous vehicle; detect a face of theuser in the first sequence of video frames; extract a first facialcharacteristic of the user from the first sequence of video frames inBlock S110; and pass the first facial characteristic into an emotioncharacterization model (or “sentiment model”) to identify the baselineemotional state of the user proximal the first time in Block S112, asshown in FIG. 2. The autonomous vehicle can later: record a secondsequence of video frames via the camera during a next segment of thetrip; detect the face of the user in the second sequence of videoframes; extract a second facial characteristic of the user from thesecond sequence of video frames in Block S130; and pass the secondfacial characteristic into the emotion characterization model toidentify the second emotional state of the user during this time inBlock S132.

As described below, the autonomous vehicle can then: detect transitionfrom the baseline emotional state including a positive emotion (e.g.,excited, elated, ecstatic, calm, serene, or content) to the secondemotional state including a negative emotion (e.g., angry, anxious,scared, sad, bored, tired); and modify a motion planning parameterimplemented by the autonomous vehicle to reduce frequency of magnitudesof navigational events occurring concurrently with this degradation inuser sentiment. Alternatively, if the autonomous vehicle detects thatthe user has transitioned from the baseline emotional state including ahigh-intensity positive emotion (e.g., excited, elated, or ecstatic) tothe second emotional state including a low-intensity positive emotion(e.g., calm, serene, or content), the autonomous vehicle can: predictthat the user is comfortable and calm in the autonomous vehicle; andmaintain current motion planning parameters accordingly.

5. Sentiment Change Response

The autonomous vehicle can: store a baseline sentiment of the user;regularly compare the user's current sentiment to the stored baselinesentiment (or the last sentiment of the user) for the current trip; andselectively adjust motion planning parameters implemented by theautonomous vehicle in (near) real-time responsive to these sentimentdeviations.

5.1 Baseline Sentiment

As described above, the autonomous vehicle can also store a sentiment ofthe user—derived from an image of the passenger compartment recordedjust after the user entered the autonomous vehicle—as a baselinesentiment of the user, as shown in FIGS. 1 and 2. The autonomous vehiclecan then respond to negative deviations in the user's sentiment fromthis baseline sentiment by modifying its motion planning and/ornavigation parameters, as described below.

5.2 Absolute Sentiment Change Response

The autonomous vehicle can regularly compare the user's currentsentiment to the stored baseline sentiment for the current trip andrespond to these deviations in real-time. In one implementation, if theautonomous vehicle detects an increase in frustration in the user abovea baseline frustration, the autonomous vehicle can increase peakpermitted accelerations in forward, reverse, and lateral directions. Inanother implementation, if the autonomous vehicle determines that theuser's level of nausea has increased, the autonomous vehicle can reducepeak permitted accelerations in forward and lateral directions andincrease weight of a smoothing function for a path planning modelimplemented by the autonomous vehicle in order to smooth a pathtraversed by the autonomous vehicle during the remainder of the trip.

In another implementation shown in FIG. 2, if the autonomous vehicledetermines that the user's fear, stress, or anxiety has increased, theautonomous vehicle can increase an obstacle avoidance distance andreduce peak permitted speed (or reduce peak permitted speed relative toposted speed limits) of the autonomous vehicle. For example, theautonomous vehicle can: autonomously navigate past a fixed obstacle at afirst offset distance according to a current motion planning parameterat a particular time during the trip; detect degradation of usersentiment in the form of increased fear, increased stress, or increasedanxiety over a short time interval leading up to or spanning theparticular time in Block S140; and correlate navigation by theautonomous vehicle past the fixed obstacle at the first offset distance(the “navigational characteristic” of the autonomous vehicle) withdegradation of sentiment of the user (e.g., increased fear, increasedstress, or increased anxiety in the user) in Block S142. The autonomousvehicle can then modify the motion planning parameter (or generate a newmotion planning parameter) that specifies a second offset distancegreater than the first offset distance.

In a similar example, the autonomous vehicle can: brake at a first rateand over a first distance according to a current motion planningparameter while approaching stopped (or slow) traffic ahead over a shorttime interval during the trip; detect degradation of user sentiment inthe form of increased fear, increased stress, or increased anxiety overthis time interval in Block S140; and correlate deceleration at thefirst rate and/or over the first distance when approaching stoppedtraffic ahead (the “navigational characteristic” of the autonomousvehicle) with degradation of sentiment of the user (e.g., increasedfear, increased stress, or increased anxiety in the user) in Block S142.The autonomous vehicle can then modify the motion planning parameter (orgenerate a new motion planning parameter) that specifies deceleration ata second rate less than the first rate and/or deceleration over a seconddistance greater than the first distance when approaching stoppedtraffic ahead.

Similarly, the autonomous vehicle can: autonomously navigate through aturn at a first steering angle and at a first speed—thereby yielding afirst angular velocity according to a current motion planningparameter—over a short time interval during the trip; detect degradationof user sentiment in the form of increased fear, increased stress,increased anxiety, or increased nausea over this time interval in BlockS140; and correlate the first angular velocity (the “navigationalcharacteristic” of the autonomous vehicle) with degradation of sentimentof the user (e.g., increased fear, increased stress, increased anxiety,or increased nausea in the user) in Block S142. The autonomous vehiclecan then modify the motion planning parameter (or generate a new motionplanning parameter) that specifies a second maximum permitted angularvelocity less than the first angular velocity when executing turns.

In another implementation, if the autonomous vehicle determines that theuser's level of nausea has increased, the autonomous vehicle can reducepeak permitted accelerations in forward and lateral directions andincrease weight of a smoothing function for a motion planning modelimplemented by the autonomous vehicle in order to smooth a pathtraversed by the autonomous vehicle during the remainder of the trip.

In yet another implementation, if the autonomous vehicle detects anincrease in frustration of the user above a baseline frustration, theautonomous vehicle can increase peak permitted accelerations in forward,reverse, and lateral directions.

The autonomous vehicle can then implement these adjusted motion planningand/or navigation parameters immediately until the conclusion of thetrip. The autonomous vehicle can also write these adjusted motionplanning and/or navigation parameters to the user's profile, such as inreal-time or upon conclusion of the trip.

The autonomous vehicle can repeat this process regularly throughout thetrip. Also, if the autonomous vehicle determines that the user'ssentiment has improved since the user entered the autonomous vehicle,the autonomous vehicle can reset the baseline sentiment for this trip tothis improved sentiment and implement this revised baseline sentimentfor the remainder of the trip or until revised again.

Furthermore, the autonomous vehicle can upload the trip and usersentiment data—such as in the form of a sentiment-annotatedgeoreferenced route—to the remote computer system for furtherprocessing, extraction of correlations between sentiment andnavigational characteristics, and further revision of motion planningand/or navigation parameters written to the user's profile.

5.3 Rate of Sentiment Change

In another implementation the autonomous vehicle can: derive rates ofsentiment change of the user; link rapid user sentiment changes withnavigation characteristics of the autonomous vehicle occurring overshort periods of time (e.g., by correlating rapid increases in useranxiety with rapid braking, the autonomous vehicle passing aconstruction zone with minimal distance offset, or merging intotraffic); link slow user sentiment changes with longer-term navigationcharacteristics of the autonomous vehicle (e.g., by correlating a slowincrease in user anxiety with excessive acceleration, angularvelocities, and jerk); and then selectively adjust motion planningparameters during the remainder of the trip to improve user sentiment orprevent further user sentiment degradation (e.g., by braking soon,increasing offset distances from construction zones, merging intotraffic slower, or increasing weight of a smoothing function forexecuting navigational actions).

For example, during the trip, the autonomous vehicle can: implement apath planning model to generate a sequence of navigational actions;apply a smoothing function at a first weight—defined by a motionplanning parameter—to transition between navigational actions in thesequence of navigational actions; and execute this sequence ofnavigational actions—smoothed according to the motion planningparameter—to autonomously navigate toward a destination location of thetrip. Throughout this trip, the autonomous vehicle can also: record atimeseries of navigational events at the autonomous vehicle during thetrip; record a timeseries of user emotions or sentiments during thetrip; and calculate a rate of change (e.g., degradation) of sentiment ofthe user during the trip (e.g., based on the timeseries of useremotions). Then, in this example, if the rate of change of degradationof the current sentiment of the user remains below a threshold rate ofchange (and if an absolute change of the user's sentiment from thebaseline sentiment exceeds a threshold magnitude), the autonomousvehicle can: correlate the current degradation of sentiment of the userwith a first jerk maximum (the “navigational characteristic”) exhibitedby the autonomous vehicle during the trip in Block S142; modify themotion planning parameter to increase the weight of the smoothingfunction in Block S150; and then implement this modified motion planningparameter to reduce the peak jerk experienced by the user during theremainder of the trip. In particular, if the autonomous vehicle deminesthat the user's nausea has slowly increased over the duration of thetrip, the autonomous vehicle can modify the path planning parameter tosmooth navigational actions executed by the autonomous vehicle, whichmay alleviate the user's nausea.

However, in this example, if the rate of change of degradation of thecurrent sentiment of the user exceeds the threshold rate of change (andif the absolute change of the user's sentiment from the baselinesentiment exceeds a threshold magnitude), the autonomous vehicle can:predict an association between the degradation of sentiment and asingular navigational event by the autonomous vehicle; scan thetimeseries of navigational events for a particular navigational eventoccurring proximal a time that this rate of sentiment degradationexceeded the threshold rate of change; and correlate this degradation ofsentiment of the user with a navigational characteristic that producedor is otherwise associated with the particular navigational event.Accordingly, the autonomous vehicle can define or refine a motionplanning parameter that is predicted to reduce the frequency of similarnavigational events (i.e., navigational events analogous to theparticular navigational event). Therefore, if the autonomous vehicledetects a rapid increase in the user's fear or anxiety (which maymanifest as similar facial expression, heart rate, heart ratevariability, and/or skin temperature changes as increased nausea), theautonomous vehicle can predict that an individual navigational eventproduced this degradation of user sentiment and modify the motionplanning parameter to avoid similar or analogous situations in thefuture.

6. Grab Handles

In yet another example implementation, the autonomous vehicle includes apassenger grab handle arranged inside the passenger compartment. Theautonomous vehicle can also include: a position sensor coupled to thegrab handle and configured to detect selection of the grab handle;and/or a force sensor configured to detect a magnitude of force appliedto the grab handle. The autonomous vehicle can thus predict user anxietyor fear based on outputs of the position and/or force sensors. Forexample, the autonomous vehicle can regularly sample these sensorsduring the user's trip. In this example, because the user may braceherself against the grab handle in preparation for a perceived impact,the autonomous vehicle can predict increased user anxiety or fear if:the autonomous vehicle is in motion; a duration of use of the grabhandle exceeds a threshold duration; and/or a force applied to the grabhandle exceeds a threshold magnitude.

The autonomous vehicle can then scan a scene nearby for an approachingor nearby vehicle, pedestrian, or other obstacle that may be result ofthe user's increased fear and increase an obstacle avoidance distancefor the user if such an obstacle is detected; if not, the autonomousvehicle can associate the user's increased fear with a speed of theautonomous vehicle and automatically reduce its current speedaccordingly.

The autonomous vehicle can therefore regularly sample the positionand/or force sensors in the grab handle, correlate outputs of thesesensors with increased user anxiety or fear, and store times anddurations of these grab handle events—and predicted instances ofelevated user fear—such as by annotating the autonomous vehicle'sgeoreferenced route toward the user specified destination.

In this implementation, the grab handle can also include a heart ratemonitor, skin temperature sensor, or biometric sensor; and theautonomous vehicle computer network reach biosignals from the userthrough the grab handle when gripped by the user and implement methodsand techniques described above to modify motion planning and/ornavigation parameters accordingly.

In a similar example, the autonomous vehicle can: detect selection of agrab handle in the passenger compartment by the user while theautonomous vehicle is in motion; interpret selection of the grab handleby the user as an increase in anxiety of the user; scan a scene near theautonomous vehicle for an object approaching the autonomous vehicle(e.g., based on data collected by exterior-facing sensors on theautonomous vehicle) at the current time; predict an association betweenthe increase in anxiety of the user and the object; modify a motionplanning parameter of the autonomous vehicle to increase a target offsetdistance from external objects in response to this association betweenuser anxiety and the approaching object; and then navigate past theobject at this new target offset distance according to the updatedmotion planning parameter.

The autonomous vehicle can implement similar methods and techniques tocollect and respond to user biosignal data via other sense-enabledsurfaces within the passenger compartment, such as an armrest, a centerconsole surfaces, a seat surface, etc.

However, the autonomous vehicle can automatically collect user-relateddata during the trip and can implement any other method or techniques tointerpret or estimate the user's sentiment at corresponding times basedon these user-related data.

7. Direct Feedback

In one variation, the autonomous vehicle collects sentiment feedbackfrom the user directly through surveys.

7.1 Selective Surveys

In one implementation, the autonomous vehicle presents sentiment-relatedsurveys to the user throughout the trip, such as through the nativeapplication executing on the user's mobile computing device, asdescribed above, or through a user interface (e.g., a touch display)arranged inside the passenger compartment.

In one implementation, the autonomous vehicle serves sentiment-relatedsurveys to the user intermittently, such as in response to theautonomous vehicle approaching and/or passing select types of obstacles,intersections, or other navigational actions during the trip. Forexample, as the autonomous vehicle approaches a known construction zoneor other obstacle in or near the roadway (e.g., approximately one halfmile or 500 meters ahead of the autonomous vehicle), the autonomousvehicle can prompt the user to indicate her current comfort level at afirst time. The autonomous vehicle can again survey the user for hersentiment at a second time once the autonomous vehicle passes theobstacle, such as within 50 meters of the autonomous vehicle passing theconstruction zone. In this example, if the user's comfort diminishedsignificantly from the first time to the second time, the autonomousvehicle can predict that the autonomous vehicle navigated too close tothe obstacle for the user's comfort and then increase an obstacleavoidance distance implemented by the autonomous vehicle during theremainder of the current trip accordingly. (The autonomous vehicle canalso store this increased obstacle avoidance distance in the user'sprofile, which other autonomous vehicles can implement to tailor theirnavigation characteristics to the user when the user is occupying theseother autonomous vehicles in the future.) However, if the user'sresponses to these surveys indicate that the user's comfort level hadnot changed over this period of time or if the user neglected to respondto the second survey—which may also indicate that the user's comfortlevel has not changed—the autonomous vehicle can preserve the currentobstacle avoidance distance or even reduce the obstacle avoidancedistance in order to simplify future motion planning by the autonomousvehicle during the current trip.

In this example, the autonomous vehicle can store the user's responsesin memory, such as by tagging segments of the current route with surveyresponses based on times that these responses were submitted by the userand geospatial locations of the autonomous vehicle at these times.

Therefore, the autonomous vehicle can survey the user for her comfortlevel as the autonomous vehicle approaches a known or detected obstacleor key road feature, such as: a construction zone; a busy or difficultintersection; an unprotected left turn; a right turn on red; a schoolzone; a highway on-ramp or off-ramp; a large pothole; etc. Theautonomous vehicle can then derive a change in navigation parameters ofthe autonomous vehicle to improve the user's comfort based on the user'sresponses to these surveys. The autonomous vehicle can also survey theuser upon initial entry into the autonomous vehicle and store asentiment extracted from the user's feedback to this initial survey as abaseline sentiment.

7.2 Continuous and Regular Surveys

In another implementation, the autonomous vehicle can regularly orcontinuously serve a query to the user for sentiment-related feedback

For example, the autonomous vehicle can render a query including “howwas my driving over the last minute?” and qualitative or qualitativemeasures (e.g., a quantitative scale of “1” through “5” of a qualitativescale of “great” to “terrible”) on a display facing the user's seatinside the autonomous vehicle. In a similar example, the autonomousvehicle can render a query including “how are you feeling?” and possiblequalitative responses (e.g., “nauseated,” “fearful,” “bored,” “fine,”“great”). If the user fails to respond to such prompts, the autonomousvehicle can predict that the user is comfortable with navigationparameters currently implemented by the autonomous vehicle and continueto implement these navigation parameters accordingly. The autonomousvehicle can similarly continue to implement these navigationcharacteristics responsive to positive confirmation from the user thatthe user is comfortable or otherwise not negatively disposed to currentautonomous navigation of the autonomous vehicle.

However, if the user does respond directly—and negatively—to thisprompt, the autonomous vehicle can automatically modify motion planningand/or navigation parameters implemented by the autonomous vehicleduring the remainder of the trip accordingly, as described below.

As described above, the autonomous vehicle can store the user'sresponses in memory, such as by linking the user's responses tocorresponding segments of the current route.

7.3 Post-Ride User Feedback

The autonomous vehicle can additionally or alternatively present asurvey related to the user's sentiment to the user—through the displayinside the passenger compartment—upon conclusion of the trip.Alternatively, the remote computer system can interface with the nativeapplication executing on the user's mobile computing device to presentthis survey to the user and to record the user's responses.

For example, the autonomous vehicle (or the native application) canpresent to the user a short list of questions, such as relating to ridecomfort, perception of safety, and experienced nausea, etc. Theautonomous vehicle can then confirm a peak permitted acceleration and anobject offset distance—implemented by the autonomous vehicle during thetrip—for the user if the user confirms a high degree of perceivedsafety. However, if the user indicates a lower perception of safety, theautonomous vehicle can: scan stored navigation data for peakacceleration(s) and minimum external object (e.g., other vehicle,pedestrian) proximity during the user's trip; reduce peak permittedaccelerations in the user's profile to below the peak accelerationduring the trip; and increase the obstacle avoidance distance in theuser's profile to more than the minimum external object proximity duringthe trip. Similarly, if the user's indicates a feeling of nausea in thepost-ride survey, the autonomous vehicle can update the user's profileto include lower peak permitted accelerations and rules for smoothernavigation paths.

In another example, the autonomous vehicle (or the native application)can render the trip route on an electronic map and prompt the user totag or annotate segments of the route with the user's comfort level,nausea, perception of safety, or other relative feedback during thesesegments of the ride. The user may then tag discrete points or segmentsalong the route that the user recalls as resulting in certainsentiments. The autonomous vehicle can then access trip datacorresponding to locations proximal each point or along each segment ofthis route thus labeled by the user and correlate the user's feedbackwith these navigation and trajectory data. For example, the autonomousvehicle can retrieve an average speed, peak lateral acceleration, peaklongitudinal acceleration, and/or proximity to a nearest detectedobstacle within a segment of a route—from the user's last trip—annotatedby the user with a negative emotion or feeling. In this example, if theuser labels the segment of the route with a feeling of nausea, theautonomous vehicle can: characterize accelerations and a trajectory ofthe autonomous vehicle over this segment; write a peak permittedacceleration less than peak accelerations along this segment to theuser's profile; and write a rule to yield smoother navigation paths thanthat which occurred over this segment of the route to the user'sprofile.

Therefore, in this variation, the autonomous vehicle can predict orderive links between sentiment-related feedback provided by the user andcertain navigational characteristics or navigational events occurring atthe autonomous vehicle, such as according to a rules engine or othermodel. The autonomous vehicle and/or the remote computer system can thenadjust motion planning and/or navigation parameters stored in the user'sprofile accordingly.

7.4 Hybrid Biosignals and User Feedback

In one variation, the autonomous vehicle collects both biosignal and/oremotion data and feedback from the user ride and refines motion planningparameters for the user based on these data. The autonomous vehicle canthen implement these modified motion planning parameters in real-timeduring this trip; additionally or alternatively, other autonomousvehicles can implement these modified motion planning parameters whenoccupied by the user during subsequent trips.

In one implementation, during the trip, the autonomous vehicle: recordsa sequence of video frames via a camera arranged in the autonomousvehicle and facing a passenger compartment in the autonomous vehicle;detects the user in the sequence of video frames; extracts a sequence offeatures of the user from the sequence of video frames; implementsmethods and techniques described above to transform the sequence offeatures into a timeseries of emotions of the user during the trip; andidentifies the baseline emotional state of the user—including a positiveemotion (e.g., excited, elated, ecstatic, calm, serene, or content)—atthe beginning of this timeseries of emotions. In this implementation,the autonomous vehicle can also record a timeseries of navigationalcharacteristics of the autonomous vehicle during the trip, such as:motion of the autonomous vehicle; brake, accelerator, and steering wheelpositions of the autonomous vehicle; constellations of object types inthe vicinity of the autonomous vehicle; local road and trafficconditions; states of nearby traffic signals; etc. The autonomousvehicle can then implement deep learning, machine learning, regression,clustering, or other data processing techniques to identify correlationsbetween navigational characteristics in the timeseries of navigationalcharacteristics and concurrent emotions of the user in the timeseries ofemotions, such as including isolated particular navigationalcharacteristics that occur with high frequency concurrently, justbefore, or just after degradation of the user's sentiment during thetrip. The autonomous vehicle can then implement methods and techniquesdescribed above to modify motion planning parameters implemented by theautonomous vehicle, such as in real-time, based on these correlations.

Furthermore, in this implementation, the autonomous vehicle (or theremote computer system) can: generate a map depicting the routetraversed by the autonomous vehicle during the trip; link segments ofthe route depicted in the map to concurrent emotions of the user basedon the timeseries of emotions and to concurrent navigationalcharacteristics of the autonomous vehicle based on the timeseries ofnavigational characteristics during the trip; and then prompt the userto confirm emotions linked to various segments of the route depicted inthe map. For example, the autonomous vehicle can: render the map on aninterior touchscreen in the autonomous vehicle; overlay the route andthe autonomous vehicle's current location on the map; annotate segmentsof the route with predicted user emotions (e.g., “calm,” “nauseated,”“anxious”) and concurrent navigational characteristics (e.g., “braking,”“passing construction zone,” “entering school zone,” “waiting forpedestrian to cross”); and prompt the user to confirm these predictedemotions and/or confirm a relationship between these emotions andconcurrent navigational characteristics by selecting, modifying, ordiscarding these annotations in (near) real-time. Alternatively, theautonomous vehicle (or the remote computer system) can serve this map,route overlay, and emotional and navigational characteristic annotationsto the user's smartphone or other computing device; and the userconfirm, modify, or discard these annotations through her smartphone,such as in (near) real-time or upon conclusion of the trip.

The autonomous vehicle (or the remote computer system) can thenimplement methods and techniques described above and below to: derivecorrelations between confirmed negative user emotions—linked to certainsegments of the route—and a subset of navigational characteristicsoccurring along these same route segments; and then modify a set ofglobal motion planning parameters for a fleet of autonomous vehicles todeviate from this subset of navigational characteristics correlated withthese negative emotions. The autonomous vehicle—and other autonomousvehicles in the fleet—can then implement these modified global motionplanning parameters when occupied by the same user and/or other usersduring subsequent trips.

8. Rider Profile

As described above, the autonomous vehicle can store motion planningparameters—modified based on changes in user sentiment or confirmedbased on user sentiment during the trip—in a rider profile associatedwith the user in Block S160.

During the trip, the autonomous vehicle can also verify that amodification to motion planning parameters executed by the autonomousvehicle improved the user's sentiment (or reduced rate of degradation ofthe user's sentiment) before writing this modification to the user'sprofile. For example, after detecting degradation of the user'ssentiment, correlation of this degradation of user sentiment with anavigational characteristic, and modifying a motion planning parameterin order to deviate from this navigational characteristic whiletraversing a first segment of the trip, the autonomous vehicle canautonomously navigate along a second segment of the trip according tothis modified motion planning parameter. During this second segment ofthe trip, the autonomous vehicle can implement methods and techniquesdescribed above to derive, access, and/or track the emotional state ofthe user. If the autonomous vehicle detects an improvement of sentimentof the user during this second segment of the trip, the autonomousvehicle can correlate this improvement of user sentiment with deviationfrom the foregoing navigational characteristic and thus verify that themodified path planning parameter—which yielded this deviation from thenavigational characteristic—improved user sentiment. Accordingly, theautonomous vehicle can write the modified motion planning parameter tothe user's profile and upload the user's profile to a remote database.

The autonomous vehicle can also correlate degradation of the user'ssentiment with multiple possible navigational characteristics,selectively adjust corresponding motion planning parameters throughoutthe trip, and verify and discard certain motion planning parametermodifications based on subsequent changes in the user's sentiment. Forexample, the autonomous vehicle can implement a first motion planningparameter and a second motion planning parameter during a first segmentof the trip. Responsive to detecting degradation of user sentiment alongthis first segment of the trip in Block S140, the autonomous vehicle cancorrelate this degradation of user sentiment with the first motionplanning parameter in Block S142 and modify the first motion planningparameter accordingly in Block S150. Then, during a second segment ofthe trip, the autonomous vehicle can: implement the modified firstmotion planning parameter and the original second motion planningparameter to autonomously navigate toward the specified destinationlocation in Block S120; continue to track the user's sentiment (e.g.,based on additional biosignals of the user collected by the interiorcamera or the wearable device worn by the user) in Block S132; detectinga second degradation of user sentiment based on a difference of a secondemotional state of the user and a previous emotional state of the user(e.g., the baseline emotional state or an emotional state prior toimplementing the modified first motion planning parameter) in BlockS140; and then correlate this second degradation of user sentiment witha second navigational characteristic exhibited of the autonomous vehicleduring both the first segment and the second segment of the trip inBlock S142. Given that further degradation of user sentiment occurredafter modifying the first motion planning parameter and that theautonomous vehicle exhibited the second navigational characteristicduring this period of user sentiment degradation, the autonomous vehiclecan: return the modified first motion planning parameter back to itsoriginal value or definition; and modifying a second motion planningparameter predicted to deviate from the second navigationalcharacteristic in Block S150. During a subsequent segment of the trip,the autonomous vehicle can: autonomously navigate toward the destinationlocation according to the original first motion planning parameter andthe modified second motion planning parameter in Block S122; and repeatthis process to confirm or modify the same or other motion planningparameters implemented by the autonomous vehicle. The autonomous vehiclecan also calculate greater confidence scores for modified motionplanning parameters and improved user sentiment if these modified motionplanning parameters result in improvement of degradation of usersentiment. Finally, the autonomous vehicle can store a final set ofmotion planning parameters implemented by the autonomous vehicle uponconclusion of the ride and/or store higher-confidence motion planningparameters in the user's profile.

Later, when the user enters a second autonomous vehicle at the beginningof a second trip, the second autonomous vehicle can: access the user'sprofile; and autonomously navigate toward a second destination locationof the second trip according to the modified motion planning parameterstored in the user's profile.

9. Example

In one example, as the autonomous vehicle approaches an obstacle in ornear the autonomous vehicle's path (e.g., construction zone), theautonomous vehicle can: detect an increase in the user's heart rate,which may indicate user discomfort; detect the user gripping a grabhandle inside the autonomous vehicle, which may indicate fear oranxiety; or detect a change in the user's physiognomy that indicates aheightened sense of fear or anxiety. If the autonomous vehicle has notyet reached the obstacle, the autonomous vehicle can: decrease itsspeed; set an increased obstacle avoidance distance; recalculate itspath according to this increased obstacle avoidance distance; andexecute this revised path. As the autonomous vehicle thus modifies itsautonomous navigation in (near-) real-time responsive to the user'ssentiment change, the autonomous vehicle can also continue to track theuser's sentiment to confirm that indicators of fear or anxiety havediminished; if so, the autonomous vehicle can write this new obstacleavoidance distance to the user's profile. However, if the user'sincreased fear or anxiety persists well past the obstacle, theautonomous vehicle can present a survey to the user to gain furtherinsight into the user's current sentiment, such as by directly queryingthe user for her perception of safety and providing options for manuallymodifying navigation parameters of the autonomous vehicle. If the userconfirms a sufficient perception of safety, the autonomous vehicle canassociate the increased fear or anxiety to external factors and returnto the previous obstacle avoidance distance.

In the foregoing example, if the autonomous vehicle has already passedthe obstacle upon calculating the revised obstacle avoidance distance,the autonomous vehicle can implement this revised obstacle avoidancedistance during the remainder of the trip and implement similar methodsto confirm that this revised obstacle avoidance distance has resulted inimproved user sentiment when navigating past other obstacles.

Furthermore, if the autonomous vehicle confirms that this revisedobstacle avoidance distance results in improved user sentimentthroughout the remainder of the trip, the autonomous vehicle can writethis revised obstacle avoidance distance to the user's profile; and thesame or other autonomous vehicles can implement this increased obstacleavoidance distance when calculating trajectories and navigating pastobstacles during future trips with the user.

Alternatively, if the autonomous vehicle detects no or only minimalincrease in the user's heart rate, no use of the grab handle, and/or nominimal change in the user's physiognomy as the autonomous vehicleapproaches this obstacle, the autonomous vehicle can: interpret thisconsistent user sentiment as user comfort with the autonomous vehicle'strajectory toward and past the obstacle; and thus confirm the currentobstacle avoidance distance in the user's profile

Furthermore, in this example, the autonomous vehicle can monitor theuser's awareness of the obstacle by tracking the user's eye as theautonomous vehicle approaches the obstacle. If the user's eye positionor movement is directed out of the autonomous vehicle and in thedirection of an upcoming or nearby obstacle for at least a minimalduration of time (e.g., two seconds), the autonomous vehicle candetermine that the user is aware of (i.e., has observed) the obstacle.If the autonomous vehicle also determines that the user has exhibitedminimal or no increase in heart rate, perspiration, or other indicatorof increased anxiety as the autonomous vehicle approaches and thenpasses the obstacle, the autonomous vehicle can: confirm that the useris generally comfortable with the autonomous vehicle's avoidance of theobstacle; and can preserve a current obstacle avoidance distance in theuser's profile accordingly.

However, if the autonomous vehicle determines that the user is not awareof or otherwise has failed to observe the obstacle, the autonomousvehicle can withhold confirming the user's comfort with the obstacleavoidance distance implemented by the autonomous vehicle. Alternatively,the autonomous vehicle can: correlate the user's lack of awareness withthe obstacle with a high degree of user comfort with the autonomousvehicle's autonomous navigation; and then update the user's profile toreflect a smaller obstacle avoidance distance—greater than or equal to aminimal obstacle avoidance distance hardcoded into operation of theautonomous vehicle—accordingly.

Furthermore, if the autonomous vehicle confirms that the user is awareof the obstacle but is also exhibiting indicators of increased fear oranxiety as the autonomous vehicle approaches the obstacle, theautonomous vehicle can: interpret the user's negative shift in sentimentwith discomfort with the autonomous vehicle's trajectory toward theobstacle; and increase the obstacle avoidance distance in the user'sprofile accordingly, as described above.

The autonomous vehicle can implement similar methods and techniques torespond to detected indicators of nausea in the user by: reducing peakforward and lateral accelerations of the autonomous vehicle; smoothingits path; and confirming that these changes in motion planning andnavigation parameters have correlated with decreased severity ofindicators of nausea in the user (or at least correlated with abatementof further increases in the user's nausea). The autonomous vehicle canalso respond to detected indicators of boredom or frustration in theuser by: increasing peak forward and lateral accelerations of theautonomous vehicle; reducing weight of a smoothing function applied tothe autonomous vehicle's path; increasing peak permitted top speed (suchas relative to posted speed limits); and confirming that these changesin motion planning and navigation parameters have correlated withdecreased boredom or frustration.

The autonomous vehicle can also execute each of these processes toaddress fear, anxiety, nausea, frustration, and other user sentimentsthroughout the trip in order to converge on motion planning andnavigation parameters best suited to the user such as for the currenttrip or more generally, thereby customizing operation of the autonomousvehicle or autonomous vehicles generally for the user.

Furthermore, the remote computer system can: compile motion planning andnavigation parameters customized for a corpus of users by a fleet ofautonomous vehicles over time; extract trends from these data; anddevelop a default profile for new users based on these trends. Theremote computer system can additionally or alternatively: generaterevised global motion planning and navigation parameters for allautonomous vehicles in the fleet or all autonomous vehicles in ageographic region based on these trends; and push these revised globalmotion planning and navigation parameters to these autonomous vehiclesin order to yield improved comfort for users during trips in theseautonomous vehicles.

10. Variation: Population Modeling

In one variation shown in FIG. 3, the autonomous vehicle collectssentiment and navigational characteristic data during a trip and uploadsthese data to a remote computer system, such as in real-time or uponconclusion of the trip and in the form of synchronized sentiment andnavigational characteristic timeseries. The remote computer system canthen: aggregate these sentiment and navigational characteristic datafrom this trip with sentiment and navigational characteristic data fromother trips completed across a fleet of autonomous vehicles; implementdeep learning, machine learning, regression, clustering, and/or otherdata processing techniques to derive correlations between ridersentiments and navigational characteristics of autonomous vehicle tripsacross a population of riders; and update or modify global motionplanning parameters in order to reduce frequency of navigationalcharacteristics correlated with negative rider sentiments anddegradation of rider sentiment. The remote computer system can pushthese updated or modified motion planning parameters to autonomousvehicles in this fleet, which can then implement these updated ormodified motion planning parameters during subsequent trips in order tomaintain greater levels of rider comfort.

In one implementation, throughout a trip, the autonomous vehicle cantrack emotions, emotion changes, and/or emotion intensities during thetrip. For example, the autonomous vehicle can: record a sequence ofvideo frames via a camera arranged in the autonomous vehicle and facinga passenger compartment in the autonomous vehicle during the trip;detect the user in the sequence of video frames; extract a sequence offeatures (e.g., facial expressions, eye movements, heart rate, skintemperature) of the user from this sequence of video frames; and thentransform this sequence of features into a timeseries of emotions of theuser (e.g., calm, nausea, anxiety) during the trip.

The autonomous vehicle can additionally or alternatively accessbiosignal and/or emotion data from a wearable device worn by the userand store these data as a timeseries of emotions of the user, asdescribed above. The autonomous vehicle can also collect rider feedbackfrom the user, such as via a survey served to the user during the ride(e.g., via the user's smartphone or an interior display in theautonomous vehicle) and/or after the ride (e.g., via the user'ssmartphone). The autonomous vehicle can then verify or modify thetimeseries of user emotions according to these data received from thewearable device and/or based on feedback provided directly by the user.

In this implementation, throughout the trip, the autonomous vehicle canalso track navigational characteristics of the autonomous vehicle, suchas: motion characteristics of the autonomous vehicle (e.g.,acceleration, velocity, geospatial location, angular velocity);autonomous vehicle navigational actions (e.g., turning, braking,accelerating, merging); cabin characteristics (e.g., air temperature,humidity, HVAC setting, window positions, cabin noise, stereo setting);local road characteristics perceived by the autonomous vehicle (e.g.,proximity of a construction zone)); local road characteristics stored ina localization map implemented by the autonomous vehicle (e.g., a laneoccupied by the autonomous vehicle, approaching a blind turn,approaching an unprotected left turn, proximity of a crosswalk,proximity of a retail shop or other institution); local obstaclesperceived by the autonomous vehicle (e.g., a constellation of othervehicles and pedestrians around the autonomous vehicle and theirvelocities or predicted trajectories); and/or rider demographics (e.g.,age, gender, number of riders); etc. The autonomous vehicle can storethese navigational characteristics in a “trip feature timeseries” ofnavigational characteristics or as timestamped navigational features,such as in a buffer or trip file for the trip. The autonomous vehiclecan then upload these rider emotion and navigational characteristic datato the remote computer system, such as in real-time or upon conclusionof the trip via a cellular or local wireless network.

The remote computer system can then populate a set of vectors withnavigational characteristic data from this trip and label these vectorswith corresponding user emotion data. For example, the remote computersystem can: segment a trip into a sequence of trip periods, such asspanning 100-meter segments of the route traversed by the autonomousvehicle or spanning ten-second intervals throughout the trip; compilenavigational characteristics data spanning each of these trip periodsinto one trip vector; and label each trip period with a predominateemotion or a constellation of emotions and emotion intensities exhibitedby the user during the trip period.

Alternatively, the remote computer system can: identify a set of“emotion periods” in the timeseries of user emotions, wherein eachemotion period is characterized by one predominant emotion or emotionintensity range (e.g., greater than or less than 5/10 intensity ofanxiety, fear, or serenity) exhibited continuously by the user; andsegment the trip feature timeseries into a set of trip feature periodsconcurrent with this set of emotion periods. For each trip period, theremote computer system can then: compile navigational characteristicdata in the trip period into one trip vector; and label the vector witha predominant emotion or emotion intensities exhibited by the userduring the concurrent emotion period. In particular, the remote computersystem can generate a set of trip vectors, wherein each trip vectorrepresents navigational characteristics during a segment of the trip inwhich the user exhibits relatively consistent emotions or sentiment andwherein consecutive trip vectors are segmented by changes in useremotions or sentiments, as shown in FIG. 3. For example, the remotecomputer system can: generate a first trip vector spanning athirty-second interval leading up to an emotion change (e.g., from calmto anxious; from less than 2/10 fearful to more than 6/10 fearful); anda second trip vector spanning a ten-second interval immediatelyfollowing the emotion change and in which the user exhibits thisdifferent emotion (e.g., anxious; more than 4/10 fearful). The remotecomputer system can also normalize emotion or sentiment labels for eachof these trip vectors (or normalize the timeseries of user emotions)based on the baseline sentiment of the user upon entering the autonomousvehicle at the beginning of the trip.

The remote computer system can repeat this process to generate a corpusof trip vectors for this particular trip and then store this set of tripvectors in a corpus of trip vectors representative of segments of tripscompleted by autonomous vehicles over time (e.g., thousands of rides inwhich thousands of different riders occupy hundreds of autonomousvehicles within an autonomous vehicle fleet over days, weeks, or years).

Based on this corpus of trip vectors labeled with (normalized) emotionsor sentiments exhibited by users during trip segments represented bythese trip vectors, the remote computer system can derive correlationsbetween rider emotions and navigational characteristics. For example,the autonomous vehicle can implement deep learning, machine learning,regression, clustering, and/or other data processing techniques todetect patterns, links, or correlations between vector features (e.g.,nearby objects, motion of the autonomous vehicle, upcoming or currentnavigational actions executed by the autonomous vehicle) and rideremotions, emotion intensities, and/or emotion changes, such as for allriders or for certain rider demographics (e.g., certain age groups andgenders).

The remote computer system can then modify motion planningparameters—such as for all autonomous vehicles in the fleet, autonomousvehicles in a certain geographic region, or for certain riderdemographics—based on these correlations between rider emotions andnavigational characteristics in order to increase frequency and durationof positive emotions and decrease frequency and duration of negativeemotions for riders occupying these autonomous vehicles. For example,the autonomous vehicle can: modify global motion planning parameters forautonomous vehicles in the fleet in order to: reduce weights of motionplanning parameters associated with or predicted to yield navigationalcharacteristics correlated with negative emotions (e.g., increase offsetdistances from nearby objects, increase braking distance whenapproaching stopped traffic); and increase weights of motion planningparameters associated with or predicted to yield navigationalcharacteristics correlated with positive emotions (e.g., increase weightof a smoothing function for executing right turns). The remote computersystem can then serve these updated global motion planning parameters toautonomous vehicles in the fleet, which can then implement these globalmotion planning parameters during subsequent trips, as shown in FIG. 3.

The remote computer system can also derive these correlations betweenrider emotions and navigational characteristics and confirm or furthermodify global motion planning parameters accordingly over time. Forexample, the remote computer system can isolate a correlation betweenmerging in a high-traffic condition and rapid increase in rider anxiety.The remote computer system can then: test this correlation by deployingautonomous vehicles to merge faster and merge slower in similarconditions during subsequent trips; and process rider emotion andnavigational characteristic data from these trips to further verify astrength of this correlation between merging speed in high-trafficconditions and increase in rider anxiety.

In another example, the remote computer system can isolate a correlationbetween braking upon approach to stopped or slowed traffic and rapidincrease in rider anxiety or fear. The remote computer system can then:test this correlation by deploying autonomous vehicles to brake soonerand at a reduced rate and vice versa in similar conditions duringsubsequent trips; and process rider emotion and navigationalcharacteristic data from these trips to further verify a strength ofthis correlation between braking ahead of stopped or slowed traffic andrider anxiety or fear.

In yet another example, the remote computer system can isolate acorrelation between entering a school zone with children present and aslow increase in rider anxiety. The remote computer system can then:test this correlation by deploying autonomous vehicles to navigatethrough school zones at reduced speeds when children are present; andprocess rider emotion and navigational characteristic data from thesetrips to further verify a strength of this correlation between speedthrough a school zone and rider anxiety.

In another example, the remote computer system can isolate a correlationbetween a particular location and increase in rider excitement. Theremote computer system can then: test this correlation by routingautonomous vehicles closer to this particular location and vice versa;and process rider emotion and navigational characteristic data fromthese trips to further verify a strength of this correlation between theparticular location and rider excitement. Responsive to verifying astrong correlation between this particular location and increased riderexcitement, the remote computer system can: identify a retail shop(e.g., a coffee shop) near this particular location; and promptautonomous vehicles traversing routes near this particular locationduring future trips to query riders to stop at the retail shop.

The remote computer system can similarly: test correlations betweenpassing road construction and increased rider anxiety by dispatchingautonomous vehicles to pass road construction zones at different speedsand offset distances; test correlations between pedestrian proximity andincreased rider anxiety by dispatching autonomous vehicles to waitlonger for pedestrians and to pass pedestrians at greater offsetdistances; test correlations between high road speeds and increasedrider anxiety by dispatching autonomous vehicles to execute trips atlower maximum speeds and vice versa; etc.

In the foregoing examples, the remote computer system can then furthermodify motion planning parameters for autonomous vehicles in the fleetbased on these additional rider emotional and navigationalcharacteristic data collected by autonomous vehicles during these trips.In particular, the remote computer system can revise motion planningparameters—the entire autonomous vehicle fleet, for autonomous vehiclesin certain geographic regions, and/or for certain rider demographics—andpush these updated motion planning parameters to these autonomousvehicles over time based on rider emotional and navigationalcharacteristic data collected by these autonomous vehicles.

The systems and methods described herein can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a human annotator computer ormobile device, wristband, smartphone, or any suitable combinationthereof. Other systems and methods of the embodiment can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated by computer-executable components integrated with apparatusesand networks of the type described above. The computer-readable mediumcan be stored on any suitable computer readable media such as RAMs,ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives,floppy drives, or any suitable device. The computer-executable componentcan be a processor but any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

I claim:
 1. A method for customizing motion characteristics of anautonomous vehicle for a user comprising: detecting a first set ofbiosignals of the user following entry of the user into the autonomousvehicle at a first time proximal a start of a trip; identifying abaseline emotional state of the user based on the first set ofbiosignals; during a first segment of the trip: autonomously navigatingtoward a destination location of the trip according to a first motionplanning parameter; detecting a second set of biosignals of the user ata second time; identifying a second emotional state of the user based onthe second set of biosignals; detecting a degradation of sentiment ofthe user based on a difference between the baseline emotional state andthe second emotional state; and correlating the degradation of sentimentof the user with a navigational characteristic of the autonomous vehicleproximal the second time; modifying the first motion planning parameterof the autonomous vehicle to define a second motion planning parameterdeviating from the navigational characteristic; and during a secondsegment of the trip, autonomously navigating toward the destinationlocation according to the second motion planning parameter.
 2. Themethod of claim 1: wherein correlating the degradation of sentiment ofthe user with the navigational characteristic of the autonomous vehicleproximal the second time comprises: identifying the navigationalcharacteristic comprising navigation by the autonomous vehicle past afixed obstacle at a first offset distance, according to the first motionplanning parameter, over a period of time comprising the second time;and correlating navigation by the autonomous vehicle past the fixedobstacle at the first offset distance with degradation of sentiment ofthe user; and wherein modifying the first motion planning parameter ofthe autonomous vehicle to define the second motion planning parametercomprises defining the second motion planning parameter specifying asecond offset distance greater than the first offset distance.
 3. Themethod of claim 1: wherein correlating the degradation of sentiment ofthe user with the navigational characteristic of the autonomous vehicleproximal the second time comprises: identifying the navigationalcharacteristic comprising deceleration over a first distance whenapproaching stopped traffic ahead, according to the first motionplanning parameter, during the second time; and correlating decelerationover the first distance when approaching stopped traffic ahead withdegradation of sentiment of the user; and wherein modifying the firstmotion planning parameter of the autonomous vehicle to define the secondmotion planning parameter comprises defining the second motion planningparameter specifying deceleration over a second distance greater thanthe first distance when approaching stopped traffic ahead.
 4. The methodof claim 1: wherein correlating the degradation of sentiment of the userwith the navigational characteristic of the autonomous vehicle proximalthe second time comprises: identifying the navigational characteristiccomprising execution of a turn at a first angular velocity, according tothe first motion planning parameter, during the second time; andcorrelating execution of the turn at the first angular velocity withdegradation of sentiment of the user; and wherein modifying the firstmotion planning parameter of the autonomous vehicle to define the secondmotion planning parameter comprises defining the second motion planningparameter specifying a maximum permitted angular velocity less than thefirst angular velocity when executing turns.
 5. The method of claim 1:further comprising: calculating a rate of change of degradation ofsentiment of the user based on biosignals recorded during the trip; andrecording a timeseries of navigational events at the autonomous vehicleduring the trip; wherein correlating the degradation of sentiment of theuser with the navigational characteristic of the autonomous vehiclecomprises, in response to the rate of change of degradation of sentimentof the user exceeding a threshold rate of change: predicting anassociation between the degradation of sentiment and a singularnavigational event by the autonomous vehicle; scanning the timeseries ofnavigational events for a particular navigational event occurringproximal the second time; and correlating the degradation of sentimentof the user with the navigational characteristic associated with theparticular navigational event; wherein modifying the first motionplanning parameter of the autonomous vehicle to define the second motionplanning parameter comprises defining the second motion planningparameter predicted to reduce frequency of navigational events analogousto the particular navigational event.
 6. The method of claim 1: whereinautonomously navigating toward the destination location of the tripaccording to the first motion planning parameter comprises: autonomouslynavigating toward the destination location of the trip according to asequence of navigational actions output by a path planning modelimplemented by the autonomous vehicle; and applying a smoothing functionat a first weight, defined by the first motion planning parameter, totransition between navigational actions in the sequence of navigationalactions; further comprising calculating a rate of change of degradationof sentiment of the user based on biosignals recorded during the trip;wherein correlating the degradation of sentiment of the user with thenavigational characteristic of the autonomous vehicle comprisescorrelating the degradation of sentiment of the user with thenavigational characteristic comprising a first jerk maximum in responseto the rate of change of degradation of sentiment of the user fallingbelow a threshold rate of change and exceeding a threshold magnitude;and wherein modifying the first motion planning parameter of theautonomous vehicle to define the second motion planning parametercomprises defining the second motion planning parameter containing asecond weight greater than the first weight of the smoothing function.7. The method of claim 1, further comprising: storing the second motionplanning parameter in a rider profile associated with the user; at asecond autonomous vehicle: accessing the rider profile; and in responseto entry of the user into the second autonomous vehicle during a secondtrip, autonomously navigating toward a second destination location ofthe second trip according to the second motion planning parameter storedin the rider profile.
 8. The method of claim 7: further comprising,during the second segment of the trip: detecting a third set ofbiosignals of the user at a third time; and identifying a thirdemotional state of the user based on the third set of biosignals;further comprising, in response to detecting an improvement of sentimentof the user based on a difference between the second emotional state andthe third emotional state, correlating the improvement of sentiment ofthe user with deviation from the navigational characteristic by theautonomous vehicle according to the second motion planning parameter;and wherein storing the second motion planning parameter in the riderprofile comprises storing the second motion planning parameter in therider profile in response to correlating the improvement of sentiment ofthe user with deviation from the navigational characteristic.
 9. Themethod of claim 1: wherein autonomously navigating toward thedestination location during the second segment of the trip comprisesautonomously navigating toward the destination location during thesecond segment of the trip according to the second motion planningparameter and a third motion planning parameter; further comprising,during the second segment of the trip: detecting a third set ofbiosignals of the user at a third time during the trip; identifying athird emotional state of the user based on the third set of biosignals;detecting a second degradation of sentiment of the user based on adifference between the second emotional state and the third emotionalstate; correlating the second degradation of sentiment of the user witha second navigational characteristic exhibited by the autonomous vehicleproximal the second time and the third time; further comprising:replacing the second motion planning parameter with the first motionplanning parameter; modifying the third motion planning parameter of theautonomous vehicle to define a fourth motion planning parameterdeviating from the second navigational characteristic; and during athird segment of the trip, autonomously navigating toward thedestination location according to the first motion planning parameterand the fourth motion planning parameter.
 10. The method of claim 1:wherein detecting the first set of biosignals of the user comprises: inresponse to entry of the user into the autonomous vehicle at the firsttime prior to the start of the trip, recording a first sequence of videoframes via a camera arranged in the autonomous vehicle and facing apassenger compartment in the autonomous vehicle; detecting a face of theuser in the first sequence of video frames; detecting a first sequenceof cyclical variations in color intensity of pixels depicting the faceof the user in the first sequence of video frames; and transforming thefirst sequence of cyclical variations in color intensity into a firstheart rate of the user proximal the first time; wherein identifying thebaseline emotional state of the user based on the first set ofbiosignals comprises identifying the baseline emotional staterepresenting a baseline anxiety of the user at the first time based onthe first heart rate; wherein detecting the second set of biosignals ofthe user at the second time during the trip comprises: recording asecond sequence of video frames via the camera; detecting the face ofthe user in the second sequence of video frames; detecting a secondsequence of cyclical variations in color intensity of pixels depictingthe face of the user in the first sequence of video frames; andtransforming the second sequence of cyclical variations in colorintensity into a second heart rate of the user proximal the second time;wherein identifying the second emotional state of the user based on thesecond set of biosignals comprises identifying the second emotionalstate representing a second anxiety of the user at the second time basedon the second heart rate; and wherein detecting the degradation ofsentiment of the user comprises detecting increased anxiety of the userbased on the difference between the baseline emotional state and thesecond emotional state.
 11. The method of claim 1: wherein detecting thefirst set of biosignals of the user comprises: in response to entry ofthe user into the autonomous vehicle at the first time prior to thestart of the trip, recording a first sequence of video frames via acamera arranged in the autonomous vehicle and facing a passengercompartment in the autonomous vehicle; detecting a face of the user inthe first sequence of video frames; and extracting a first facialcharacteristic of the user from the first sequence of video frames; andwherein identifying the baseline emotional state of the user from thefirst facial characteristic of the user comprises passing the firstfacial characteristic into an emotion characterization model to identifythe baseline emotional state of the user proximal the first time;wherein detecting the second set of biosignals of the user at the secondtime during the trip comprises: recording a second sequence of videoframes via the camera; detecting the face of the user in the secondsequence of video frames; and extracting a second facial characteristicof the user from the second sequence of video frames; and whereinidentifying the second emotional state of the user based on the secondset of biosignals comprises passing the second facial characteristicinto the emotion characterization model to identify the second emotionalstate of the user proximal the second time; and wherein detecting thedegradation of sentiment of the user comprises detecting one of:transition from the baseline emotional state comprising a positiveemotion at the first time to the second emotional state comprising anegative emotion at the second time; and transition from the baselineemotional state comprising a high-intensity positive emotion at thefirst time to the second emotional state comprising a low-intensitypositive emotion at the second time.
 12. The method of claim 1: whereindetecting the first set of biosignals of the user comprises: in responseto entry of the user into the autonomous vehicle at the first time priorto the start of the trip, recording a first sequence of video frames viaa camera arranged in the autonomous vehicle and facing a passengercompartment in the autonomous vehicle; detecting eyes of the user in thefirst sequence of video frames; and detecting a first sequence of rapideye movements of the user in the first sequence of video frames; andwherein identifying the baseline emotional state of the user based onthe first set of biosignals comprises: estimating a first degree ofnausea of the user proximal the first time based on a first rate of eyemovements in the first sequence of rapid eye movements; and storing thefirst degree of nausea of the user as the baseline emotional state ofthe user; wherein detecting the second set of biosignals of the user atthe second time during the trip comprises: recording a second sequenceof video frames via the camera; detecting eyes of the user in the secondsequence of video frames; and detecting a second sequence of rapid eyemovements of the user in the second sequence of video frames; andwherein identifying the second emotional state of the user based on thesecond set of biosignals comprises: estimating a second degree of nauseaof the user proximal the second time based on a second rate of eyemovements in the second sequence of rapid eye movements; and storing thesecond degree of nausea of the user as the second emotional state of theuser; and wherein detecting the degradation of sentiment of the usercomprises detecting increased nausea of the user based on the differencebetween the baseline emotional state and the second emotional state. 13.The method of claim 1: wherein detecting the first set of biosignals anddetecting the second set of biosignals of the user during the tripcomprises: during the trip, recording a sequence of video frames via acamera arranged in the autonomous vehicle and facing a passengercompartment in the autonomous vehicle; detecting the user in thesequence of video frames; extracting a sequence of features of the userfrom the sequence of video frames; and transforming the sequence offeatures into a timeseries of emotions of the user during the trip;wherein identifying the baseline emotional state of the user comprisesidentifying the baseline emotional state, in the timeseries of emotionsof the user, comprising a positive emotion; further comprising recordinga timeseries of navigational characteristics of the autonomous vehicleduring the trip; wherein correlating the degradation of sentiment of theuser with the navigational characteristic of the autonomous vehicleproximal the second time comprises identifying the second emotionalstate, in the timeseries of emotions of the user, comprising a negativeemotion approximately concurrent with the navigational characteristic inthe timeseries of navigational characteristics.
 14. The method of claim13, further comprising: generating a map depicting a route traversed bythe autonomous vehicle during the trip; linking segments of the routedepicted in the map to concurrent emotions of the user based on thetimeseries of emotions and to concurrent navigational characteristics ofthe autonomous vehicle based on the timeseries of navigationalcharacteristics during the trip; prompting the user to confirm emotionslinked to segments of the route depicted in the map; derivingcorrelations between a set of negative emotions, linked to segments ofthe route, and a set of navigational characteristics stored in thetimeseries of navigational characteristics; modifying a set of globalmotion planning parameters for a fleet of autonomous vehicles to deviatefrom the set of navigational characteristic correlated with the set ofnegative emotions; and when occupied by a second user during a secondtrip, autonomously navigating according to the set of global motionplanning parameters.
 15. The method of claim 1: wherein detecting thefirst set of biosignals and detecting the second set of biosignals ofthe user during the trip comprises: during the trip, recording asequence of video frames via a camera arranged in the autonomous vehicleand facing a passenger compartment in the autonomous vehicle; detectingthe user in the sequence of video frames; extracting a sequence offeatures of the user from the sequence of video frames; and transformingthe sequence of features into a timeseries of emotions of the userduring the trip; and further comprising: recording a trip featuretimeseries of navigational characteristics of the autonomous vehicle andnearby objects perceived by the autonomous vehicle during the trip;identifying a set of emotion periods in the timeseries of emotions, eachemotion period in the set of emotion periods characterized by onepredominant emotion exhibited by the user; segmenting the trip featuretimeseries into a set of trip feature periods concurrent with the set ofemotion periods; labeling each trip feature period, in the set of tripfeature periods, with a predominant emotion exhibited by the user duringthe concurrent emotion period; storing the set of trip feature periodsin a corpus of trip feature periods representative of segments of tripscompleted by a fleet of autonomous vehicles over time; based on thecorpus of trip feature periods, deriving correlations between rideremotions, navigational characteristics, and nearby objects; modifying aset of global motion planning parameters for the fleet of autonomousvehicles to: reduce weights of navigational characteristics correlatedwith negative emotions; and increase offset distances from nearbyobjects correlated with negative emotions; and serving the set of globalmotion planning parameters to autonomous vehicles in the fleet ofautonomous vehicles.
 16. The method of claim 1: wherein detecting thefirst set of biosignals of the user comprises: in response to entry ofthe user into the autonomous vehicle at the first time prior to thestart of the trip, connecting to a wearable device worn by the user viashort-range wireless communication protocol, the wearable devicecomprising a biometric sensor; and accessing the first set of biosignalsrecorded and wirelessly broadcast by the wearable device proximal thefirst time; and wherein detecting the second set of biosignals of theuser comprises accessing the second set of biosignals recorded andwirelessly broadcast by the wearable device proximal the second time.17. The method of claim 1: further comprising detecting a third set ofbiosignals of a second user following entry of the second user into theautonomous vehicle at approximately the first time; identifying a secondbaseline emotional state of the second user based on the third set ofbiosignals; during the first segment of the trip: detecting a fourth setof biosignals of the second user at approximately the second time duringthe trip; identifying a fourth emotional state of the second user basedon the fourth set of biosignals; detecting a second degradation ofsentiment of the second user based on a difference between the secondbaseline emotional state and the fourth emotional state of the seconduser; and correlating a second degradation of sentiment of the seconduser with the navigational characteristic of the autonomous vehicleproximal the second time; and wherein modifying the first motionplanning parameter of the autonomous vehicle to define the second motionplanning parameter comprises confirming the second motion planningparameter responsive to correlating the second degradation of sentimentof the second user with the navigational characteristic.
 18. The methodof claim 1, further comprising, during a third segment of the trip whilethe autonomous vehicle is in motion: detecting selection of a grabhandle in the passenger compartment by the user; interpreting selectionof the grab handle by the user as an increase in anxiety of the user;scanning a scene near the autonomous vehicle for an object approachingthe autonomous vehicle; predicting an association between the increasein anxiety of the user and the object; modifying a third motion planningparameter of the autonomous vehicle to increase a target offset distancefrom external objects; and navigating past the object at the targetoffset distance according to the third motion planning parameter.
 19. Amethod for customizing motion characteristics of an autonomous vehiclefor a user comprising: accessing a baseline emotional state of the userfollowing entry of the user into the autonomous vehicle at a first timeproximal a start of a trip; during a first segment of the trip:generating a first sequence of navigational actions according to a firstmotion planning parameter; executing the first sequence of navigationalactions to autonomously navigate toward a destination location;accessing a second emotional state of the user at a second time;detecting a degradation of sentiment of the user based on a differencebetween the baseline emotional state and the second emotional state; andcorrelating the degradation of sentiment of the user with a navigationalcharacteristic of the autonomous vehicle proximal the second time;modifying the first motion planning parameter of the autonomous vehicleto define a revised motion planning parameter deviating from thenavigational characteristic; and during a second segment of the trip:generating a second sequence of navigational actions according to thesecond motion planning parameter; and executing the second sequence ofnumber value actions to autonomously navigate toward the destinationlocation.
 20. The method of claim 19: wherein accessing the baselineemotional state of the user comprises: detecting the user in a firstsequence of video frames recorded by a camera, arranged in theautonomous vehicle, following entry of the user into the autonomousvehicle; and estimating the baseline emotional state comprising abaseline anxiety level based on features extracted from the firstsequence of video frames; wherein accessing the second emotional stateof the user comprises: detecting the user in a second sequence of videoframes recorded by the camera during the trip; and estimating the secondemotional state comprising a second anxiety level based on featuresextracted from the second sequence of video frames; wherein detectingthe degradation of sentiment of the user comprises detecting an increasein anxiety of the user based on a difference between the baselineanxiety level and the second anxiety level; wherein correlating thedegradation of sentiment of the user with the navigationalcharacteristic of the autonomous vehicle comprises correlating thedegradation of sentiment of the user with proximity of the autonomousvehicle to an object in a particular object class at approximately thesecond time; and wherein modifying the first motion planning parameterof the autonomous vehicle to define the revised motion planningparameter comprises generating the revised motion planning parameterspecifying increased offset distance between the autonomous vehicle andobjects in the particular object class.